code: def load_state_dict(self, state_dict: Dict, blocking: bool = True):
      _buffer = io.BytesIO()
      torch.save(state_dict, _buffer)
      state_stream = _buffer.getvalue()
      state_id = ray.put(state_stream)
  
      remote_calls = [
          worker.load_state_stream.remote(state_id)
          for worker in self.remote_workers
      ]
      if blocking:
          ray.get(remote_calls)
  
imports: from bigdl.orca.learn.ray_estimator import BaseEstimator
  from bigdl.orca.ray import OrcaRayContext
  from bigdl.orca.data.file import enable_multi_fs_save, enable_multi_fs_load
  import io
  import torch
  import ray
  from ray.exceptions import RayActorError
  from abc import abstractmethod, ABCMeta
  from bigdl.orca.learn.pytorch.utils import find_free_port, check_for_failure
  from bigdl.orca.learn.utils import get_driver_node_ip
  from bigdl.dllib.utils.log4Error import invalidInputError, logging
  from typing import Dict
label_desc: Use image node_id 9 for calling the BaseRayEstimator obj's underlying member method code with example usage: obj.load_state_dict(state_dict, blocking) without return types